Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation.

Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a $450$-m outdoor walk of $32$ study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online.

Fixed-effects inference and tests of correlation for longitudinal functional data.

We propose an inferential framework for fixed effects in longitudinal functional models and introduce tests for the correlation structures induced by the longitudinal sampling procedure. The framework provides a natural extension of standard longitudinal correlation models for scalar observations to functional observations. Using simulation studies, we compare fixed effects estimation under correctly and incorrectly specified correlation structures and also test the longitudinal correlation structure. Finally, we apply the proposed methods to a longitudinal functional dataset on physical activity. The computer code for the proposed method is available at https://github.com/rli20ST758/FILF.

Cardiovascular mortality risk prediction using objectively measured physical activity phenotypes in NHANES 2003-2006.

Increased physical activity (PA) has been associated with a decreased risk of cardiovascular disease (CVD) and mortality. However, most previous studies use self-reported PA instead of objectively measured PA assessed by wearable accelerometers. To the best of our knowledge, there have not been studies that quantified the univariate and multivariate ability of objectively measured PA summaries to predict the risk of CVD mortality. We investigate the ability of objectively measured PA summary variables to predict CVD mortality: as individual predictors, as part of the best multivariate model incorporating traditional predictors, and as additions to the best multivariate model using only traditional CVD predictors. Data were collected in the National Health and Nutrition Examination Survey 2003-2006 waves for US participants aged 50-85. The predictive ability was measured using Concordance, sometimes referred to as the C-statistic. Specifically, we calculated 10-fold cross-validated concordance (CVC) in survey-weighted Cox proportional hazard models. The best univariate predictor of CVD mortality was total activity count (outperformed age). In multivariate models, two of the eight predictors identified using the improvement in CVC threshold of 0.001 were PA measures (CVC = 0.844). The best model without physical activity (7 predictors) had CVC of 0.830. The addition of PA measures to the best traditional model was significantly better at predicting CVD mortality (P < 0.001). Accelerometer-derived PA measures have excellent cardiovascular mortality prediction performance. Wearable accelerometers have a potential for assessment of individuals' CVD mortality risks.

HIV Infection Is Associated With Variability in Ventricular Repolarization: The Multicenter AIDS Cohort Study (MACS).

BACKGROUND: People living with human immunodeficiency virus (HIV+) have greater risk for sudden arrhythmic death than HIV-uninfected (HIV-) individuals. HIV-associated abnormal cardiac repolarization may contribute to this risk. We investigated whether HIV serostatus is associated with ventricular repolarization lability by using the QT variability index (QTVI), defined as a log measure of QT-interval variance indexed to heart rate variance. METHODS: We studied 1123 men (589 HIV+ and 534 HIV-) from MACS (Multicenter AIDS Cohort Study), using the ZioXT ambulatory electrocardiography patch. Beat-to-beat analysis of up to 4 full days of electrocardiographic data per participant was performed using an automated algorithm (median analyzed duration [quartile 1-quartile 3]: 78.3 [66.3-83.0] hours/person). QTVI was modeled using linear mixed-effects models adjusted for demographics, cardiac risk factors, and HIV-related and inflammatory biomarkers. RESULTS: Mean (SD) age was 60.1 (11.9) years among HIV- and 54.2 (11.2) years among HIV+ participants (P<0.001), 83% of whom had undetectable (<20 copies/mL) HIV-1 viral load (VL). In comparison with HIV- men, HIV+ men had higher QTVI (adjusted difference of +0.077 [95% CI, +0.032 to +0.123]). The magnitude of this association depended on the degree of viremia, such that in HIV+ men with undetectable VL, adjusted QTVI was +0.064 (95% CI, +0.017 to +0.111) higher than in HIV- men, whereas, in HIV+ men with detectable VL, adjusted QTVI was higher by +0.150 (95% CI, 0.072-0.228) than in HIV- referents. Analysis of QTVI subcomponents showed that HIV+ men had: (1) lower heart rate variability irrespective of VL status, and (2) higher QT variability if they had detectable, but not with undetectable, VL, in comparison with HIV- men. Higher levels of C-reactive protein, interleukin-6, intercellular adhesion molecule-1, soluble tumor necrosis factor receptor 2, and soluble cluster of differentiation-163 (borderline), were associated with higher QTVI and partially attenuated the association with HIV serostatus. CONCLUSIONS: HIV+ men have greater beat-to-beat variability in QT interval (QTVI) than HIV- men, especially in the setting of HIV viremia and heightened inflammation. Among HIV+ men, higher QTVI suggests ventricular repolarization lability, which can increase susceptibility to arrhythmias, whereas lower heart rate variability signals a component of autonomic dysfunction.

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.

Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.

The upstrap.

The bootstrap, introduced in Efron (1979. Bootstrap methods: another look at the jackknife. The Annals of Statistics7, 1-26), is a landmark method for quantifying variability. It uses sampling with replacement with a sample size equal to that of the original data. We propose the upstrap, which samples with replacement either more or fewer samples than the original sample size. We illustrate the upstrap by solving a hard, but common, sample size calculation problem. The data and code used for the analysis in this article are available on GitHub (2018. https://github.com/ccrainic/upstrap).

Progressive Multiple Sclerosis Is Associated with Faster and Specific Retinal Layer Atrophy.

OBJECTIVE: Therapeutic development in progressive multiple sclerosis (PMS) has been hampered by a lack of reliable biomarkers to monitor neurodegeneration. Optical coherence tomography (OCT)-derived retinal measures have been proposed as promising biomarkers to fulfill this role. However, it is unclear whether retinal atrophy persists in PMS, exceeds normal aging, or can be distinguished from relapsing-remitting multiple sclerosis (RRMS). METHODS: 178 RRMS, 186 PMS, and 66 control participants were followed with serial OCT for a median follow-up of 3.7 years. RESULTS: The estimated proportion of peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell + inner plexiform layer (GCIPL) thinning in multiple sclerosis (MS) attributable to normal aging increased from 42.7% and 16.7% respectively at age 25 years, to 83.7% and 81.1% at age 65 years. However, independent of age, PMS was associated with faster pRNFL (-0.34 ± 0.09%/yr, p < 0.001) and GCIPL (-0.27 ± 0.07%/yr, p < 0.001) thinning, as compared to RRMS. In both MS and controls, higher baseline age was associated with faster inner nuclear layer (INL) and outer nuclear layer (ONL) thinning. INL and ONL thinning were independently faster in PMS, as compared to controls (INL:-0.09 ± 0.04%/yr, p = 0.03; ONL:-0.12 ± 0.06%/yr, p = 0.04), and RRMS (INL:-0.10 ± 0.04%/yr, p = 0.01; ONL:-0.13 ± 0.05%/yr, p = 0.01), whereas they were similar in RRMS and controls. Unlike RRMS, disease-modifying therapies (DMTs) did not impact rates of retinal layer atrophy in PMS. INTERPRETATION: PMS is associated with faster retinal atrophy independent of age. INL and ONL measures may be novel biomarkers of neurodegeneration in PMS that appear to be unaffected by conventional DMTs. The effects of aging on rates of retinal layer atrophy should be considered in clinical trials incorporating OCT outcomes. ANN NEUROL 2020;87:885-896.

Neuroconductor: an R platform for medical imaging analysis.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Simple fixed-effects inference for complex functional models.

We propose simple inferential approaches for the fixed effects in complex functional mixed effects models. We estimate the fixed effects under the independence of functional residuals assumption and then bootstrap independent units (e.g. subjects) to conduct inference on the fixed effects parameters. Simulations show excellent coverage probability of the confidence intervals and size of tests for the fixed effects model parameters. Methods are motivated by and applied to the Baltimore Longitudinal Study of Aging, though they are applicable to other studies that collect correlated functional data.

Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.

Many modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimation procedures. The method is motivated by and applied to a functional magnetic resonance imaging study designed to analyze the relationship between pain and brain activity.

A two-stage model for wearable device data.

Recent advances of wearable computing technology have allowed continuous health monitoring in large observational studies and clinical trials. Examples of data collected by wearable devices include minute-by-minute physical activity proxies measured by accelerometers or heart rate. The analysis of data generated by wearable devices has so far been quite limited to crude summaries, for example, the mean activity count over the day. To better utilize the full data and account for the dynamics of activity level in the time domain, we introduce a two-stage regression model for the minute-by-minute physical activity proxy data. The model allows for both time-varying parameters and time-invariant parameters, which helps capture both the transition dynamics between active/inactive periods (Stage 1) and the activity intensity dynamics during active periods (Stage 2). The approach extends methods developed for zero-inflated Poisson data to account for the high-dimensionality and time-dependence of the high density data generated by wearable devices. Methods are motivated by and applied to the Baltimore Longitudinal Study of Aging.

A practical guide to big data.

Big Data is increasingly prevalent in science and data analysis. We provide a short tutorial for adapting to these changes and making the necessary adjustments to the academic culture to keep Biostatistics truly impactful in scientific research.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Evidence of bias in randomized clinical trials of hepatitis C interferon therapies.

INTRODUCTION: Bias may occur in randomized clinical trials in favor of the new experimental treatment because of unblinded assessment of subjective endpoints or wish bias. Using results from published trials, we analyzed and compared the treatment effect of hepatitis C antiviral interferon therapies experimental or control. METHODS: Meta-regression of trials enrolling naïve hepatitis C virus patients that underwent four therapies including interferon alone or plus ribavirin during past years. The outcome measure was the sustained response evaluated by transaminases and/or hepatitis C virus-RNA serum load. Data on the outcome across therapies were collected according to the assigned arm (experimental or control) and to other trial and patient-level characteristics. RESULTS: The overall difference in efficacy between the same treatment labeled experimental or control had a mean of +11.9% (p < 0.0001). The unadjusted difference favored the experimental therapies of group IFN-1 (+6%) and group IFN-3 (+10%), while there was no difference for group IFN-2 because of success rates from large multinational trials. In a meta-regression model with trial-specific random effects including several trial and patient-level variables, treatment and arm type remained significant (p < 0.0001 and p = 0.0009 respectively) in addition to drug-schedule-related variables. CONCLUSION: Our study indicates the same treatment is more effective when labeled "experimental" compared to when labeled "control" in a setting of trials using an objective endpoint and even after adjusting for patient and study-level characteristics. We discuss several factors related to design and conduct of hepatitis C trials as potential explanations of the bias toward the experimental treatment.

Removing inter-subject technical variability in magnetic resonance imaging studies.

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.

Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.

Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging physical activity was recorded every minute for [Formula: see text] adults for an average of [Formula: see text] days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern, we introduce a practical bivariate smoother and two crucial innovations: (i) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation and (ii) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. For the analysis, we propose a practical and very fast trivariate spline smoother to estimate covariate-dependent covariances and their spectra. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.

Movement prediction using accelerometers in a human population.

We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the data across subjects by matching the standing up and lying down portions of triaxial accelerometry data. This is necessary to account for differences between the variability in the position of the device relative to gravity, which are induced by body shape and size as well as by the ambiguous definition of device placement. We also normalize the data at the device level to ensure that the magnitude of the signal at rest is similar across devices. After normalization we use overlapping movelets (segments of triaxial accelerometry time series) extracted from some of the subjects to predict the movement type of the other subjects. The problem was motivated by and is applied to a laboratory study of 20 older participants who performed different activities while wearing accelerometers at the hip. Prediction results based on other people's labeled dictionaries of activity performed almost as well as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible.

Quantitative Intracerebral Hemorrhage Localization.

BACKGROUND AND PURPOSE: The location of intracerebral hemorrhage (ICH) is currently described in a qualitative way; we provide a quantitative framework for estimating ICH engagement and its relevance to stroke outcomes. METHODS: We analyzed 111 patients with ICH from the Minimally Invasive Surgery Plus Recombinant-Tissue Plasminogen Activator for Intracerebral Evacuation (MISTIE) II clinical trial. We estimated ICH engagement at a population level using image registration of computed tomographic scans to a template and a previously labeled atlas. Predictive regions of National Institutes of Health Stroke Scale and Glasgow Coma Scale stroke severity scores, collected at enrollment, were estimated. RESULTS: The percent coverage of the ICH by these regions strongly outperformed the reader-labeled locations. The adjusted R(2) almost doubled from 0.129 (reader-labeled model) to 0.254 (quantitative location model) for National Institutes of Health Stroke Scale and more than tripled from 0.069 (reader-labeled model) to 0.214 (quantitative location model). A permutation test confirmed that the new predictive regions are more predictive than chance: P<0.001 for National Institutes of Health Stroke Scale and P<0.01 for Glasgow Coma Scale. CONCLUSIONS: Objective measures of ICH location and engagement using advanced computed tomographic imaging processing provide finer, objective, and more quantitative anatomic information than that provided by human readers. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00224770.

Validated automatic brain extraction of head CT images.

BACKGROUND: X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction - the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. AIM: To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. METHODS: All images were thresholded using a 0-100 Hounsfield unit (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ=1mm(3)) and re-thresholded to 0-100HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomated/ICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA. RESULTS: Smoothing images improves brain extraction results using BET for all measures except specificity (all p<0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p<0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p<0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p<0.001) and lower specificity (0.9981 vs. 0.9982; median difference: -0.0001, p<0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. CONCLUSION: BET performs well at brain extraction on thresholded, 1mm(3) smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided.